Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements.

Ryohei Fukuma,Masayuki Hirata,Toshiki Yoshimine,Hiroshi Yokoi,Yukiyasu Kamitani,Youichi Saitoh,Takufumi Yanagisawa,Haruhiko Kishima

doi:10.3389/fnins.2018.00478

Abstract

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training.Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks.Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33).Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.

Highlights

Our findings demonstrated that MEG-based Brain–machine interfaces (BMIs) training to control a robotic hand significantly improved the accuracy to control the robotic hand and induced significant changes of the cortical representation of hand movements in terms of classification accuracy
These results suggest that the BMI training will be useful for two important applications
The non-invasive BMI training will be beneficial in training patients before applying invasive BMI

Summary

Introduction

Brain–machine interfaces (BMIs) can reconstruct motor function in paralyzed subjects (Hochberg et al, 2006, 2012; Yanagisawa et al, 2012a; Collinger et al, 2013; Bouton et al, 2016) as well as induce functional alterations in cortical activity (Ganguly et al, 2011; Wander et al, 2013; Orsborn et al, 2014; Yanagisawa et al, 2016). A BMI works by first recording neural activity and converting the recorded activity into control of some machine, such as a robotic hand or computer (Yanagisawa et al, 2009, 2011, 2012a,b; Nakanishi et al, 2013, 2014; Fukuma et al, 2015, 2016). Recent studies demonstrated that neurofeedback training using BMI induces plastic changes in neural activities in accordance with some functional alterations in the neural system. We recently reported that BMI training to control a robotic hand induced plastic changes in the motor cortical representation of phantom limb pain patients and changed their pain in accordance with the plastic changes (Yanagisawa et al, 2016)

Methods

Results

Conclusion